Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations2149
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory554.2 KiB
Average record size in memory264.1 B

Variable types

Numeric15
Categorical18

Alerts

HeadInjury is highly imbalanced (55.5%) Imbalance
BMI has unique values Unique
AlcoholConsumption has unique values Unique
PhysicalActivity has unique values Unique
DietQuality has unique values Unique
SleepQuality has unique values Unique
CholesterolTotal has unique values Unique
CholesterolLDL has unique values Unique
CholesterolHDL has unique values Unique
CholesterolTriglycerides has unique values Unique
MMSE has unique values Unique
FunctionalAssessment has unique values Unique
ADL has unique values Unique

Reproduction

Analysis started2024-11-07 09:54:53.977474
Analysis finished2024-11-07 09:55:52.689353
Duration58.71 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct31
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.908795
Minimum60
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:52.826033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61
Q167
median75
Q383
95-th percentile89
Maximum90
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9902214
Coefficient of variation (CV)0.12001557
Kurtosis-1.189214
Mean74.908795
Median Absolute Deviation (MAD)8
Skewness0.045964341
Sum160979
Variance80.82408
MonotonicityNot monotonic
2024-11-07T09:55:53.061131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
68 84
 
3.9%
88 84
 
3.9%
72 82
 
3.8%
76 81
 
3.8%
71 80
 
3.7%
90 79
 
3.7%
67 77
 
3.6%
60 74
 
3.4%
70 74
 
3.4%
66 73
 
3.4%
Other values (21) 1361
63.3%
ValueCountFrequency (%)
60 74
3.4%
61 68
3.2%
62 70
3.3%
63 69
3.2%
64 59
2.7%
65 64
3.0%
66 73
3.4%
67 77
3.6%
68 84
3.9%
69 63
2.9%
ValueCountFrequency (%)
90 79
3.7%
89 72
3.4%
88 84
3.9%
87 68
3.2%
86 50
2.3%
85 57
2.7%
84 71
3.3%
83 71
3.3%
82 68
3.2%
81 57
2.7%

EducationLevel
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
1
854 
2
636 
0
446 
3
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

Length

2024-11-07T09:55:53.291359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:53.501740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

Most occurring characters

ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 854
39.7%
2 636
29.6%
0 446
20.8%
3 213
 
9.9%

BMI
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.655697
Minimum15.008851
Maximum39.992767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:53.785963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum15.008851
5-th percentile16.349202
Q121.611408
median27.823924
Q333.869778
95-th percentile38.856789
Maximum39.992767
Range24.983916
Interquartile range (IQR)12.25837

Descriptive statistics

Standard deviation7.2174381
Coefficient of variation (CV)0.26097473
Kurtosis-1.184508
Mean27.655697
Median Absolute Deviation (MAD)6.1650578
Skewness-0.026714591
Sum59432.093
Variance52.091413
MonotonicityNot monotonic
2024-11-07T09:55:54.054802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.28973831 1
 
< 0.1%
22.92774923 1
 
< 0.1%
26.82768119 1
 
< 0.1%
17.79588244 1
 
< 0.1%
33.80081704 1
 
< 0.1%
39.93417453 1
 
< 0.1%
33.52980969 1
 
< 0.1%
30.53151549 1
 
< 0.1%
31.21385627 1
 
< 0.1%
22.67754548 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
15.00885118 1
< 0.1%
15.0120707 1
< 0.1%
15.01465919 1
< 0.1%
15.01823993 1
< 0.1%
15.03127134 1
< 0.1%
15.03572352 1
< 0.1%
15.03674287 1
< 0.1%
15.07094446 1
< 0.1%
15.08320143 1
< 0.1%
15.08579285 1
< 0.1%
ValueCountFrequency (%)
39.99276746 1
< 0.1%
39.98851283 1
< 0.1%
39.98153263 1
< 0.1%
39.96486074 1
< 0.1%
39.9463213 1
< 0.1%
39.93417453 1
< 0.1%
39.91513616 1
< 0.1%
39.89086465 1
< 0.1%
39.88691232 1
< 0.1%
39.83774246 1
< 0.1%

Smoking
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1529 
1
620 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

Length

2024-11-07T09:55:54.279931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:54.449656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

Most occurring characters

ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1529
71.1%
1 620
28.9%

AlcoholConsumption
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.039442
Minimum0.0020030991
Maximum19.989293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:54.691751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0020030991
5-th percentile1.0175833
Q15.1398096
median9.9344124
Q315.157931
95-th percentile19.03159
Maximum19.989293
Range19.98729
Interquartile range (IQR)10.018121

Descriptive statistics

Standard deviation5.7579103
Coefficient of variation (CV)0.57352893
Kurtosis-1.2028757
Mean10.039442
Median Absolute Deviation (MAD)5.0126197
Skewness0.018414567
Sum21574.76
Variance33.153531
MonotonicityNot monotonic
2024-11-07T09:55:54.962926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.890703151 1
 
< 0.1%
13.29721773 1
 
< 0.1%
4.542523818 1
 
< 0.1%
19.55508453 1
 
< 0.1%
12.20926555 1
 
< 0.1%
9.242734045 1
 
< 0.1%
0.6916508414 1
 
< 0.1%
0.1451794071 1
 
< 0.1%
13.70914263 1
 
< 0.1%
12.12150793 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.002003099136 1
< 0.1%
0.01050443879 1
< 0.1%
0.018737728 1
< 0.1%
0.03626046707 1
< 0.1%
0.04276497959 1
< 0.1%
0.0652859968 1
< 0.1%
0.07931352887 1
< 0.1%
0.1034600068 1
< 0.1%
0.1188173667 1
< 0.1%
0.1271097563 1
< 0.1%
ValueCountFrequency (%)
19.98929336 1
< 0.1%
19.98829132 1
< 0.1%
19.98562151 1
< 0.1%
19.98401842 1
< 0.1%
19.97444257 1
< 0.1%
19.9729271 1
< 0.1%
19.96687514 1
< 0.1%
19.96667036 1
< 0.1%
19.96088756 1
< 0.1%
19.95486109 1
< 0.1%

PhysicalActivity
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9202021
Minimum0.0036160168
Maximum9.9874294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:55.242310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0036160168
5-th percentile0.4692145
Q12.5706265
median4.7664243
Q37.4278988
95-th percentile9.4586127
Maximum9.9874294
Range9.9838134
Interquartile range (IQR)4.8572724

Descriptive statistics

Standard deviation2.8571911
Coefficient of variation (CV)0.58070604
Kurtosis-1.1791965
Mean4.9202021
Median Absolute Deviation (MAD)2.4131042
Skewness0.044972594
Sum10573.514
Variance8.1635411
MonotonicityNot monotonic
2024-11-07T09:55:55.505362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.570993383 1
 
< 0.1%
6.327112474 1
 
< 0.1%
7.61988454 1
 
< 0.1%
7.844987791 1
 
< 0.1%
8.42800135 1
 
< 0.1%
0.532764611 1
 
< 0.1%
8.448133523 1
 
< 0.1%
0.9585448336 1
 
< 0.1%
4.069969216 1
 
< 0.1%
5.701564408 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.003616016826 1
< 0.1%
0.007482592358 1
< 0.1%
0.009347661532 1
< 0.1%
0.01995690585 1
< 0.1%
0.02246155975 1
< 0.1%
0.03185729452 1
< 0.1%
0.04540548725 1
< 0.1%
0.05056027776 1
< 0.1%
0.06549380991 1
< 0.1%
0.06815547175 1
< 0.1%
ValueCountFrequency (%)
9.987429413 1
< 0.1%
9.986553927 1
< 0.1%
9.985068848 1
< 0.1%
9.984089541 1
< 0.1%
9.983993817 1
< 0.1%
9.976581454 1
< 0.1%
9.97459519 1
< 0.1%
9.961248529 1
< 0.1%
9.955316019 1
< 0.1%
9.947909587 1
< 0.1%

DietQuality
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9931379
Minimum0.0093847201
Maximum9.9983457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:55.839157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0093847201
5-th percentile0.44044086
Q12.4584549
median5.0760873
Q37.5586247
95-th percentile9.4818056
Maximum9.9983457
Range9.988961
Interquartile range (IQR)5.1001698

Descriptive statistics

Standard deviation2.909055
Coefficient of variation (CV)0.58261058
Kurtosis-1.2289618
Mean4.9931379
Median Absolute Deviation (MAD)2.541761
Skewness-0.01205775
Sum10730.253
Variance8.462601
MonotonicityNot monotonic
2024-11-07T09:55:56.219707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.941403884 1
 
< 0.1%
1.347214306 1
 
< 0.1%
0.5187671387 1
 
< 0.1%
1.826334665 1
 
< 0.1%
7.43560414 1
 
< 0.1%
1.086457115 1
 
< 0.1%
5.41438408 1
 
< 0.1%
9.847203145 1
 
< 0.1%
6.148757099 1
 
< 0.1%
8.213415145 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.009384720116 1
< 0.1%
0.01264573162 1
< 0.1%
0.01305568376 1
< 0.1%
0.01433233787 1
< 0.1%
0.01644569515 1
< 0.1%
0.01993976404 1
< 0.1%
0.02454257994 1
< 0.1%
0.02541234352 1
< 0.1%
0.02897373629 1
< 0.1%
0.03210468539 1
< 0.1%
ValueCountFrequency (%)
9.998345679 1
< 0.1%
9.997202924 1
< 0.1%
9.980281203 1
< 0.1%
9.971204135 1
< 0.1%
9.97109129 1
< 0.1%
9.968027307 1
< 0.1%
9.962781707 1
< 0.1%
9.956199249 1
< 0.1%
9.954879342 1
< 0.1%
9.952450962 1
< 0.1%

SleepQuality
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0510811
Minimum4.0026287
Maximum9.9998403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:55:56.668573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4.0026287
5-th percentile4.2608327
Q15.4829972
median7.1156462
Q38.5625209
95-th percentile9.7302183
Maximum9.9998403
Range5.9972117
Interquartile range (IQR)3.0795237

Descriptive statistics

Standard deviation1.7635729
Coefficient of variation (CV)0.25011384
Kurtosis-1.2124536
Mean7.0510811
Median Absolute Deviation (MAD)1.5407098
Skewness-0.069630253
Sum15152.773
Variance3.1101895
MonotonicityNot monotonic
2024-11-07T09:55:57.040668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.878710516 1
 
< 0.1%
9.025678666 1
 
< 0.1%
7.151292743 1
 
< 0.1%
9.673574158 1
 
< 0.1%
8.392553685 1
 
< 0.1%
8.870584074 1
 
< 0.1%
4.717674095 1
 
< 0.1%
6.759128926 1
 
< 0.1%
8.772614759 1
 
< 0.1%
4.300610239 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
4.00262866 1
< 0.1%
4.004173353 1
< 0.1%
4.00617092 1
< 0.1%
4.008388179 1
< 0.1%
4.008685222 1
< 0.1%
4.011537881 1
< 0.1%
4.013579397 1
< 0.1%
4.016066782 1
< 0.1%
4.020211458 1
< 0.1%
4.022886036 1
< 0.1%
ValueCountFrequency (%)
9.999840317 1
< 0.1%
9.999201296 1
< 0.1%
9.997627265 1
< 0.1%
9.994079149 1
< 0.1%
9.993038857 1
< 0.1%
9.989399094 1
< 0.1%
9.989228061 1
< 0.1%
9.988587669 1
< 0.1%
9.988451617 1
< 0.1%
9.986317716 1
< 0.1%

FamilyHistoryAlzheimers
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1607 
1
542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

Length

2024-11-07T09:55:57.399974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:57.714541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

Most occurring characters

ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1607
74.8%
1 542
 
25.2%

CardiovascularDisease
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1839 
1
310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Length

2024-11-07T09:55:58.069472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:58.416737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1839
85.6%
1 310
 
14.4%

Diabetes
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1825 
1
324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Length

2024-11-07T09:55:58.685379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:59.059828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Depression
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1718 
1
431 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

Length

2024-11-07T09:55:59.337124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:55:59.706866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1718
79.9%
1 431
 
20.1%

HeadInjury
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1950 
1
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Length

2024-11-07T09:56:00.133514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:00.453470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1950
90.7%
1 199
 
9.3%

Hypertension
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1829 
1
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

Length

2024-11-07T09:56:00.634804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:00.851883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1829
85.1%
1 320
 
14.9%

SystolicBP
Real number (ℝ)

Distinct90
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.26477
Minimum90
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:01.068220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94
Q1112
median134
Q3157
95-th percentile175
Maximum179
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation25.949352
Coefficient of variation (CV)0.19326999
Kurtosis-1.1975928
Mean134.26477
Median Absolute Deviation (MAD)23
Skewness0.0099710423
Sum288535
Variance673.36887
MonotonicityNot monotonic
2024-11-07T09:56:01.323721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155 37
 
1.7%
106 34
 
1.6%
126 34
 
1.6%
130 33
 
1.5%
165 33
 
1.5%
160 33
 
1.5%
107 32
 
1.5%
178 32
 
1.5%
117 31
 
1.4%
105 31
 
1.4%
Other values (80) 1819
84.6%
ValueCountFrequency (%)
90 27
1.3%
91 26
1.2%
92 21
1.0%
93 21
1.0%
94 26
1.2%
95 25
1.2%
96 21
1.0%
97 24
1.1%
98 24
1.1%
99 23
1.1%
ValueCountFrequency (%)
179 24
1.1%
178 32
1.5%
177 23
1.1%
176 22
1.0%
175 23
1.1%
174 21
1.0%
173 11
 
0.5%
172 30
1.4%
171 26
1.2%
170 19
0.9%

DiastolicBP
Real number (ℝ)

Distinct60
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.847836
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:01.567459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q174
median91
Q3105
95-th percentile116
Maximum119
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.592496
Coefficient of variation (CV)0.19580323
Kurtosis-1.2350404
Mean89.847836
Median Absolute Deviation (MAD)15
Skewness-0.054469941
Sum193083
Variance309.49592
MonotonicityNot monotonic
2024-11-07T09:56:01.838832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 53
 
2.5%
62 47
 
2.2%
116 46
 
2.1%
102 46
 
2.1%
103 45
 
2.1%
64 44
 
2.0%
71 43
 
2.0%
100 43
 
2.0%
107 42
 
2.0%
87 41
 
1.9%
Other values (50) 1699
79.1%
ValueCountFrequency (%)
60 26
1.2%
61 53
2.5%
62 47
2.2%
63 35
1.6%
64 44
2.0%
65 36
1.7%
66 25
1.2%
67 34
1.6%
68 28
1.3%
69 38
1.8%
ValueCountFrequency (%)
119 38
1.8%
118 35
1.6%
117 33
1.5%
116 46
2.1%
115 35
1.6%
114 36
1.7%
113 36
1.7%
112 38
1.8%
111 38
1.8%
110 34
1.6%

CholesterolTotal
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.19752
Minimum150.09332
Maximum299.99335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:02.099621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum150.09332
5-th percentile157.36182
Q1190.25296
median225.08643
Q3262.03166
95-th percentile291.5952
Maximum299.99335
Range149.90004
Interquartile range (IQR)71.778693

Descriptive statistics

Standard deviation42.542233
Coefficient of variation (CV)0.18891075
Kurtosis-1.156042
Mean225.19752
Median Absolute Deviation (MAD)35.905273
Skewness-0.018674284
Sum483949.47
Variance1809.8416
MonotonicityNot monotonic
2024-11-07T09:56:02.364910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283.3967969 1
 
< 0.1%
242.3668397 1
 
< 0.1%
231.162595 1
 
< 0.1%
284.1818578 1
 
< 0.1%
159.5822396 1
 
< 0.1%
239.0440318 1
 
< 0.1%
177.3738505 1
 
< 0.1%
268.0818882 1
 
< 0.1%
269.3216938 1
 
< 0.1%
247.3617523 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
150.0933156 1
< 0.1%
150.1355716 1
< 0.1%
150.1921833 1
< 0.1%
150.2126499 1
< 0.1%
150.2870141 1
< 0.1%
150.4030492 1
< 0.1%
150.444945 1
< 0.1%
150.4596489 1
< 0.1%
150.5756955 1
< 0.1%
150.7539904 1
< 0.1%
ValueCountFrequency (%)
299.9933525 1
< 0.1%
299.9599914 1
< 0.1%
299.8901335 1
< 0.1%
299.8732587 1
< 0.1%
299.8684825 1
< 0.1%
299.659926 1
< 0.1%
299.6381459 1
< 0.1%
299.5060742 1
< 0.1%
299.3267144 1
< 0.1%
299.3074673 1
< 0.1%

CholesterolLDL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.33594
Minimum50.230707
Maximum199.96567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:02.626114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum50.230707
5-th percentile57.443444
Q187.195798
median123.34259
Q3161.73373
95-th percentile192.80735
Maximum199.96567
Range149.73496
Interquartile range (IQR)74.537935

Descriptive statistics

Standard deviation43.366584
Coefficient of variation (CV)0.34878558
Kurtosis-1.2083206
Mean124.33594
Median Absolute Deviation (MAD)37.132961
Skewness0.036233409
Sum267197.94
Variance1880.6606
MonotonicityNot monotonic
2024-11-07T09:56:02.909643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.20006443 1
 
< 0.1%
56.15089696 1
 
< 0.1%
193.4079955 1
 
< 0.1%
153.3227622 1
 
< 0.1%
65.36663684 1
 
< 0.1%
183.9230203 1
 
< 0.1%
194.2481348 1
 
< 0.1%
79.22955023 1
 
< 0.1%
75.79094243 1
 
< 0.1%
50.43008305 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
50.23070656 1
< 0.1%
50.28230179 1
< 0.1%
50.40000296 1
< 0.1%
50.43008305 1
< 0.1%
50.46696876 1
< 0.1%
50.48066969 1
< 0.1%
50.70707684 1
< 0.1%
50.79308655 1
< 0.1%
50.89191008 1
< 0.1%
51.09757785 1
< 0.1%
ValueCountFrequency (%)
199.9656651 1
< 0.1%
199.9365652 1
< 0.1%
199.8071792 1
< 0.1%
199.6681284 1
< 0.1%
199.4617214 1
< 0.1%
199.43785 1
< 0.1%
199.4204702 1
< 0.1%
199.3679889 1
< 0.1%
199.3589872 1
< 0.1%
199.2496067 1
< 0.1%

CholesterolHDL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.463533
Minimum20.003434
Maximum99.980324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:03.198424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum20.003434
5-th percentile24.487935
Q139.095698
median59.768237
Q378.93905
95-th percentile96.22624
Maximum99.980324
Range79.97689
Interquartile range (IQR)39.843351

Descriptive statistics

Standard deviation23.139174
Coefficient of variation (CV)0.38913217
Kurtosis-1.2178539
Mean59.463533
Median Absolute Deviation (MAD)19.940067
Skewness0.042205687
Sum127787.13
Variance535.42137
MonotonicityNot monotonic
2024-11-07T09:56:03.771254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.92004333 1
 
< 0.1%
33.6825635 1
 
< 0.1%
79.02847732 1
 
< 0.1%
69.77229186 1
 
< 0.1%
68.45749071 1
 
< 0.1%
76.67826165 1
 
< 0.1%
24.5856915 1
 
< 0.1%
85.6272617 1
 
< 0.1%
37.88314272 1
 
< 0.1%
26.10345101 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
20.00343401 1
< 0.1%
20.01512483 1
< 0.1%
20.06423997 1
< 0.1%
20.26395079 1
< 0.1%
20.36677074 1
< 0.1%
20.42235515 1
< 0.1%
20.57716178 1
< 0.1%
20.68996674 1
< 0.1%
20.72553322 1
< 0.1%
20.74260545 1
< 0.1%
ValueCountFrequency (%)
99.98032408 1
< 0.1%
99.95949425 1
< 0.1%
99.95835803 1
< 0.1%
99.93249647 1
< 0.1%
99.83690027 1
< 0.1%
99.80943675 1
< 0.1%
99.77030816 1
< 0.1%
99.76895478 1
< 0.1%
99.74207702 1
< 0.1%
99.68281638 1
< 0.1%

CholesterolTriglycerides
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.2815
Minimum50.407194
Maximum399.94186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:04.074971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum50.407194
5-th percentile67.986071
Q1137.58322
median230.30198
Q3314.83905
95-th percentile382.99946
Maximum399.94186
Range349.53467
Interquartile range (IQR)177.25582

Descriptive statistics

Standard deviation101.98672
Coefficient of variation (CV)0.4467586
Kurtosis-1.2190259
Mean228.2815
Median Absolute Deviation (MAD)88.247884
Skewness-0.032923232
Sum490576.94
Variance10401.291
MonotonicityNot monotonic
2024-11-07T09:56:04.339339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217.3968725 1
 
< 0.1%
162.1891431 1
 
< 0.1%
294.6309092 1
 
< 0.1%
83.63832414 1
 
< 0.1%
277.5773575 1
 
< 0.1%
188.4672873 1
 
< 0.1%
222.3927461 1
 
< 0.1%
385.5479636 1
 
< 0.1%
101.3323859 1
 
< 0.1%
139.7150235 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
50.40719362 1
< 0.1%
50.46161069 1
< 0.1%
50.77533975 1
< 0.1%
50.97375551 1
< 0.1%
50.99271714 1
< 0.1%
51.06422694 1
< 0.1%
51.24195102 1
< 0.1%
51.47292411 1
< 0.1%
51.9793907 1
< 0.1%
52.05209662 1
< 0.1%
ValueCountFrequency (%)
399.9418616 1
< 0.1%
399.8881376 1
< 0.1%
399.8543217 1
< 0.1%
399.7917622 1
< 0.1%
399.7296976 1
< 0.1%
399.6843109 1
< 0.1%
399.2397108 1
< 0.1%
398.9107435 1
< 0.1%
398.8557853 1
< 0.1%
398.8231931 1
< 0.1%

MMSE
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.755132
Minimum0.0053121464
Maximum29.991381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:04.598512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0053121464
5-th percentile1.5199773
Q17.1676023
median14.44166
Q322.161028
95-th percentile28.27137
Maximum29.991381
Range29.986068
Interquartile range (IQR)14.993426

Descriptive statistics

Standard deviation8.6131513
Coefficient of variation (CV)0.58373936
Kurtosis-1.2303648
Mean14.755132
Median Absolute Deviation (MAD)7.5007356
Skewness0.03238178
Sum31708.779
Variance74.186375
MonotonicityNot monotonic
2024-11-07T09:56:04.879293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.11477737 1
 
< 0.1%
21.46353236 1
 
< 0.1%
20.61326731 1
 
< 0.1%
7.356248625 1
 
< 0.1%
13.99112724 1
 
< 0.1%
0.1749467572 1
 
< 0.1%
14.61165842 1
 
< 0.1%
2.508099706 1
 
< 0.1%
9.534384697 1
 
< 0.1%
27.53514392 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.005312146442 1
< 0.1%
0.0180217112 1
< 0.1%
0.03530104148 1
< 0.1%
0.04692752935 1
< 0.1%
0.04793678683 1
< 0.1%
0.05062351247 1
< 0.1%
0.1270512526 1
< 0.1%
0.1368956617 1
< 0.1%
0.1486608797 1
< 0.1%
0.1551193311 1
< 0.1%
ValueCountFrequency (%)
29.99138056 1
< 0.1%
29.97426212 1
< 0.1%
29.9594248 1
< 0.1%
29.95081313 1
< 0.1%
29.92629877 1
< 0.1%
29.88009851 1
< 0.1%
29.83942537 1
< 0.1%
29.83399283 1
< 0.1%
29.82527167 1
< 0.1%
29.79569277 1
< 0.1%

FunctionalAssessment
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.080055
Minimum0.0004595936
Maximum9.9964671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:05.176197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0004595936
5-th percentile0.48263118
Q12.5662809
median5.0944387
Q37.5469813
95-th percentile9.5538819
Maximum9.9964671
Range9.9960075
Interquartile range (IQR)4.9807004

Descriptive statistics

Standard deviation2.8927435
Coefficient of variation (CV)0.56943153
Kurtosis-1.1830512
Mean5.080055
Median Absolute Deviation (MAD)2.5058758
Skewness-0.034576218
Sum10917.038
Variance8.3679648
MonotonicityNot monotonic
2024-11-07T09:56:05.443656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.30754331 1
 
< 0.1%
6.518876973 1
 
< 0.1%
7.118695504 1
 
< 0.1%
5.895077345 1
 
< 0.1%
8.965106304 1
 
< 0.1%
3.255908712 1
 
< 0.1%
0.478424434 1
 
< 0.1%
0.3074599769 1
 
< 0.1%
9.014623918 1
 
< 0.1%
6.019406627 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.0004595935958 1
< 0.1%
0.01189831558 1
< 0.1%
0.01321089131 1
< 0.1%
0.01518726845 1
< 0.1%
0.02042808996 1
< 0.1%
0.03668570593 1
< 0.1%
0.04425448301 1
< 0.1%
0.0449707042 1
< 0.1%
0.04498797422 1
< 0.1%
0.05366870619 1
< 0.1%
ValueCountFrequency (%)
9.996467073 1
< 0.1%
9.992609579 1
< 0.1%
9.991356786 1
< 0.1%
9.990056838 1
< 0.1%
9.986440704 1
< 0.1%
9.986410313 1
< 0.1%
9.975833337 1
< 0.1%
9.973696989 1
< 0.1%
9.964670742 1
< 0.1%
9.948450648 1
< 0.1%

MemoryComplaints
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1702 
1
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

Length

2024-11-07T09:56:05.675035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:05.861872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1702
79.2%
1 447
 
20.8%

BehavioralProblems
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1812 
1
337 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

Length

2024-11-07T09:56:06.066928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:06.243475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1812
84.3%
1 337
 
15.7%

ADL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9829584
Minimum0.0012879277
Maximum9.9997471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2024-11-07T09:56:06.460236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0012879277
5-th percentile0.39259084
Q12.3428363
median5.0389727
Q37.5814901
95-th percentile9.4719418
Maximum9.9997471
Range9.9984592
Interquartile range (IQR)5.2386538

Descriptive statistics

Standard deviation2.9497748
Coefficient of variation (CV)0.59197259
Kurtosis-1.2495009
Mean4.9829584
Median Absolute Deviation (MAD)2.6135182
Skewness-0.030435804
Sum10708.378
Variance8.7011714
MonotonicityNot monotonic
2024-11-07T09:56:06.722887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.327563008 1
 
< 0.1%
1.72588346 1
 
< 0.1%
2.592424133 1
 
< 0.1%
7.119547743 1
 
< 0.1%
6.481225859 1
 
< 0.1%
4.524796394 1
 
< 0.1%
5.794056378 1
 
< 0.1%
8.197995071 1
 
< 0.1%
5.765005858 1
 
< 0.1%
0.004354362827 1
 
< 0.1%
Other values (2139) 2139
99.5%
ValueCountFrequency (%)
0.001287927702 1
< 0.1%
0.004354362827 1
< 0.1%
0.00927364691 1
< 0.1%
0.01469122129 1
< 0.1%
0.01530582703 1
< 0.1%
0.02262624036 1
< 0.1%
0.02299916056 1
< 0.1%
0.0315346497 1
< 0.1%
0.0355907446 1
< 0.1%
0.04113651734 1
< 0.1%
ValueCountFrequency (%)
9.999747122 1
< 0.1%
9.988158571 1
< 0.1%
9.972663015 1
< 0.1%
9.96279372 1
< 0.1%
9.947440463 1
< 0.1%
9.94503638 1
< 0.1%
9.943805167 1
< 0.1%
9.940631031 1
< 0.1%
9.934789879 1
< 0.1%
9.928740566 1
< 0.1%

Confusion
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1708 
1
441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Length

2024-11-07T09:56:06.954222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:07.143080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Most occurring characters

ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1708
79.5%
1 441
 
20.5%

Disorientation
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1809 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

Length

2024-11-07T09:56:07.322658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:07.495981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1809
84.2%
1 340
 
15.8%

PersonalityChanges
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1825 
1
324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Length

2024-11-07T09:56:07.679931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:07.860632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1825
84.9%
1 324
 
15.1%

DifficultyCompletingTasks
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1808 
1
341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Length

2024-11-07T09:56:08.047752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:08.235952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1808
84.1%
1 341
 
15.9%

Forgetfulness
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1501 
1
648 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Length

2024-11-07T09:56:08.420427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:08.595501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Most occurring characters

ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1501
69.8%
1 648
30.2%

Diagnosis
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1389 
1
760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Length

2024-11-07T09:56:09.363065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:09.536266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Most occurring characters

ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1389
64.6%
1 760
35.4%

Gender_cat
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1088 
1
1061 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Length

2024-11-07T09:56:09.722452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:09.898347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Most occurring characters

ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1088
50.6%
1 1061
49.4%

Ethnicity_cat
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
1
1278 
0
871 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Length

2024-11-07T09:56:10.091553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T09:56:10.283329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Most occurring characters

ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1278
59.5%
0 871
40.5%

Interactions

2024-11-07T09:55:48.217901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:54:57.021899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:01.121841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:04.611453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:08.177940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:11.306189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:16.703506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:19.755698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:22.742446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:26.594297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:31.235588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:34.308538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:37.740001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:40.682998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:45.131839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-07T09:55:11.135446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-07T09:55:19.556678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:22.553886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:26.364630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:30.965589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:34.127744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:37.544015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:40.488986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:44.809526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-07T09:55:48.037490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-07T09:56:10.528832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ADLAgeAlcoholConsumptionBMIBehavioralProblemsCardiovascularDiseaseCholesterolHDLCholesterolLDLCholesterolTotalCholesterolTriglyceridesConfusionDepressionDiabetesDiagnosisDiastolicBPDietQualityDifficultyCompletingTasksDisorientationEducationLevelEthnicity_catFamilyHistoryAlzheimersForgetfulnessFunctionalAssessmentGender_catHeadInjuryHypertensionMMSEMemoryComplaintsPersonalityChangesPhysicalActivitySleepQualitySmokingSystolicBP
ADL1.000-0.038-0.007-0.0100.0580.0000.006-0.0200.0000.0230.0000.0000.0390.372-0.005-0.0090.0000.0000.0360.0000.0350.0230.0530.0000.0000.0240.0030.0000.051-0.0140.0140.0000.015
Age-0.0381.0000.008-0.0160.0230.0000.0080.0040.000-0.0040.0000.0000.0000.000-0.004-0.0240.0380.0000.0000.0000.0000.0000.0050.0290.0000.000-0.0040.0000.030-0.0110.0480.025-0.005
AlcoholConsumption-0.0070.0081.000-0.0090.0000.000-0.002-0.018-0.0340.0240.0220.0250.0000.043-0.0090.0200.0000.0400.0450.0000.0240.000-0.0160.0000.0000.042-0.0110.0000.0000.023-0.0040.000-0.030
BMI-0.010-0.016-0.0091.0000.0450.0000.0380.0240.002-0.0180.0000.0000.0000.034-0.0020.0200.0530.0450.0000.0000.0000.045-0.0310.0440.0000.000-0.0030.0510.0110.000-0.0060.028-0.019
BehavioralProblems0.0580.0230.0000.0451.0000.0000.0230.0170.0000.0000.0000.0000.0150.2220.0000.0380.0000.0000.0130.0000.0050.0040.0260.0000.0400.0210.0000.0000.0000.0600.0000.0000.000
CardiovascularDisease0.0000.0000.0000.0000.0001.0000.0200.0000.0530.0210.0000.0000.0000.0210.0000.0000.0080.0090.0000.0000.0000.0000.0620.0250.0000.0000.0000.0200.0220.0180.0180.0150.000
CholesterolHDL0.0060.008-0.0020.0380.0230.0201.000-0.0380.0090.0160.0460.0440.0330.0240.008-0.0090.0400.0090.0200.0110.0180.028-0.0030.0410.0150.034-0.0060.0390.000-0.0020.0140.0400.004
CholesterolLDL-0.0200.004-0.0180.0240.0170.000-0.0381.0000.010-0.0060.0000.0000.0400.000-0.016-0.0240.0000.0000.0000.0000.0360.039-0.0170.0420.0000.0000.0250.0440.0000.0180.0080.000-0.007
CholesterolTotal0.0000.000-0.0340.0020.0000.0530.0090.0101.000-0.0030.0470.0000.0550.0210.014-0.0160.0130.0210.0000.0000.0000.000-0.0070.0000.0000.000-0.0130.0420.0430.0140.0070.0000.018
CholesterolTriglycerides0.023-0.0040.024-0.0180.0000.0210.016-0.006-0.0031.0000.0000.0760.0480.053-0.0080.0350.0000.0000.0000.0320.0380.000-0.0100.0000.0380.000-0.0080.0480.0000.0270.0240.000-0.035
Confusion0.0000.0000.0220.0000.0000.0000.0460.0000.0470.0001.0000.0000.0000.0000.0000.0000.0000.0000.0430.0240.0000.0000.0000.0200.0200.0000.0000.0000.0000.0000.0000.0000.036
Depression0.0000.0000.0250.0000.0000.0000.0440.0000.0000.0760.0001.0000.0000.0000.0000.0460.0000.0000.0430.0000.0000.0000.0000.0000.0000.0210.0000.0000.0110.0520.0000.0310.000
Diabetes0.0390.0000.0000.0000.0150.0000.0330.0400.0550.0480.0000.0001.0000.0210.0140.0000.0000.0000.0000.0000.0010.0000.0390.0000.0000.0000.0000.0000.0000.0410.0370.0270.020
Diagnosis0.3720.0000.0430.0340.2220.0210.0240.0000.0210.0530.0000.0000.0211.0000.0000.0000.0000.0090.0260.0000.0230.0000.4090.0000.0000.0260.3150.3050.0000.0000.0000.0000.000
DiastolicBP-0.005-0.004-0.009-0.0020.0000.0000.008-0.0160.014-0.0080.0000.0000.0140.0001.0000.0110.0210.0510.0000.0000.0520.0000.0310.0000.0000.064-0.0270.0000.000-0.0090.0090.0310.002
DietQuality-0.009-0.0240.0200.0200.0380.000-0.009-0.024-0.0160.0350.0000.0460.0000.0000.0111.0000.0420.0350.0370.0000.0000.000-0.0090.0000.0240.0270.0210.0300.0630.0110.0510.0320.006
DifficultyCompletingTasks0.0000.0380.0000.0530.0000.0080.0400.0000.0130.0000.0000.0000.0000.0000.0210.0421.0000.0000.0000.0230.0000.0000.0570.0000.0000.0000.0170.0370.0290.0400.0490.0000.053
Disorientation0.0000.0000.0400.0450.0000.0090.0090.0000.0210.0000.0000.0000.0000.0090.0510.0350.0001.0000.0140.0000.0270.0220.0000.0000.0220.0230.0000.0000.0000.0000.0000.0160.000
EducationLevel0.0360.0000.0450.0000.0130.0000.0200.0000.0000.0000.0430.0430.0000.0260.0000.0370.0000.0141.0000.0210.0110.0000.0290.0000.0000.0630.0060.0000.0000.0000.0000.0000.000
Ethnicity_cat0.0000.0000.0000.0000.0000.0000.0110.0000.0000.0320.0240.0000.0000.0000.0000.0000.0230.0000.0211.0000.0100.0080.0230.0000.0000.0000.0000.0000.0090.0000.0380.0000.032
FamilyHistoryAlzheimers0.0350.0000.0240.0000.0050.0000.0180.0360.0000.0380.0000.0000.0010.0230.0520.0000.0000.0270.0110.0101.0000.0000.0000.0000.0000.0000.0520.0160.0000.0000.0000.0390.020
Forgetfulness0.0230.0000.0000.0450.0040.0000.0280.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0080.0001.0000.0000.0170.0000.0190.0340.0000.0000.0000.0000.0000.037
FunctionalAssessment0.0530.005-0.016-0.0310.0260.062-0.003-0.017-0.007-0.0100.0000.0000.0390.4090.031-0.0090.0570.0000.0290.0230.0000.0001.0000.0460.0000.0000.0230.0660.000-0.0010.0300.0000.012
Gender_cat0.0000.0290.0000.0440.0000.0250.0410.0420.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0461.0000.0000.0000.0610.0000.0120.0000.0000.0000.038
HeadInjury0.0000.0000.0000.0000.0400.0000.0150.0000.0000.0380.0200.0000.0000.0000.0000.0240.0000.0220.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0200.0000.0000.0000.000
Hypertension0.0240.0000.0420.0000.0210.0000.0340.0000.0000.0000.0000.0210.0000.0260.0640.0270.0000.0230.0630.0000.0000.0190.0000.0000.0001.0000.0800.0000.0000.0000.0000.0000.000
MMSE0.003-0.004-0.011-0.0030.0000.000-0.0060.025-0.013-0.0080.0000.0000.0000.315-0.0270.0210.0170.0000.0060.0000.0520.0340.0230.0610.0000.0801.0000.0000.033-0.0090.0110.000-0.004
MemoryComplaints0.0000.0000.0000.0510.0000.0200.0390.0440.0420.0480.0000.0000.0000.3050.0000.0300.0370.0000.0000.0000.0160.0000.0660.0000.0000.0000.0001.0000.0190.0000.0000.0000.000
PersonalityChanges0.0510.0300.0000.0110.0000.0220.0000.0000.0430.0000.0000.0110.0000.0000.0000.0630.0290.0000.0000.0090.0000.0000.0000.0120.0200.0000.0330.0191.0000.0000.0310.0000.000
PhysicalActivity-0.014-0.0110.0230.0000.0600.018-0.0020.0180.0140.0270.0000.0520.0410.000-0.0090.0110.0400.0000.0000.0000.0000.000-0.0010.0000.0000.000-0.0090.0000.0001.000-0.0010.000-0.004
SleepQuality0.0140.048-0.004-0.0060.0000.0180.0140.0080.0070.0240.0000.0000.0370.0000.0090.0510.0490.0000.0000.0380.0000.0000.0300.0000.0000.0000.0110.0000.031-0.0011.0000.000-0.029
Smoking0.0000.0250.0000.0280.0000.0150.0400.0000.0000.0000.0000.0310.0270.0000.0310.0320.0000.0160.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
SystolicBP0.015-0.005-0.030-0.0190.0000.0000.004-0.0070.018-0.0350.0360.0000.0200.0000.0020.0060.0530.0000.0000.0320.0200.0370.0120.0380.0000.000-0.0040.0000.000-0.004-0.0290.0001.000

Missing values

2024-11-07T09:55:51.628236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-07T09:55:52.364487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeEducationLevelBMISmokingAlcoholConsumptionPhysicalActivityDietQualitySleepQualityFamilyHistoryAlzheimersCardiovascularDiseaseDiabetesDepressionHeadInjuryHypertensionSystolicBPDiastolicBPCholesterolTotalCholesterolLDLCholesterolHDLCholesterolTriglyceridesMMSEFunctionalAssessmentMemoryComplaintsBehavioralProblemsADLConfusionDisorientationPersonalityChangesDifficultyCompletingTasksForgetfulnessDiagnosisGender_catEthnicity_cat
073222.927749013.2972186.3271121.3472149.02567900110014272242.36684056.15089733.682563162.18914321.4635326.518877001.72588300010011
189026.82768104.5425247.6198850.5187677.15129300000011564231.162595193.40799679.028477294.63090920.6132677.118696002.59242400001011
273117.795882019.5550857.8449881.8263359.67357410000099116284.181858153.32276269.77229283.6383247.3562495.895077007.11954801010010
374133.800817112.2092668.4280017.4356048.392554000000118115159.58224065.36663768.457491277.57735813.9911278.965106016.48122600000001
489020.716974018.4543566.3104610.7954985.59723800000094117237.60218492.86970056.874305291.19878013.5176096.045039000.01469100110011
586130.62688604.1401440.2110621.5849227.26195300100016862280.712539198.33462979.080503263.94365527.5175295.510144009.01568610000000
668238.38762210.6460479.2576955.8973885.47768600001014388263.73414952.47067066.533369216.4891751.9644136.062124009.23632800001010
775118.776009013.7238264.6494518.3419034.21321000000011763151.38313769.62351077.346816210.57086610.1395683.401374004.51724810001111
872027.833188012.1678481.5313606.7368825.748224000001117119233.605755144.04574043.075893151.16418625.8207327.396061010.75623200100000
987035.456302116.0286886.4407738.0860197.55177301000013078281.630050130.49758074.291247144.17597528.3884091.148904014.55439400000011
AgeEducationLevelBMISmokingAlcoholConsumptionPhysicalActivityDietQualitySleepQualityFamilyHistoryAlzheimersCardiovascularDiseaseDiabetesDepressionHeadInjuryHypertensionSystolicBPDiastolicBPCholesterolTotalCholesterolLDLCholesterolHDLCholesterolTriglyceridesMMSEFunctionalAssessmentMemoryComplaintsBehavioralProblemsADLConfusionDisorientationPersonalityChangesDifficultyCompletingTasksForgetfulnessDiagnosisGender_catEthnicity_cat
213968017.82896502.9827478.3948260.3175265.73678000011010261236.649402173.90269948.917216334.38573110.8282509.104117008.81911500010010
214089234.42241907.7706870.9475675.7321394.91776001000114278227.11122858.54891028.587889187.7893394.9264001.605154008.73408201000010
214172221.600144019.3917668.1814696.6401957.08809110000013791264.994941181.68352196.361322347.47961920.0137868.722739009.57077600001011
214288020.09760004.0894587.3896332.8787386.271699000001166105190.975712104.92057336.59371181.07513025.1409037.729270006.15604000010111
214366132.01380619.3087064.3524025.4323749.62431210000110199233.95410857.45417067.16267598.6880955.9036891.405821004.54453800011100
214461139.12175701.5611264.0499646.5553067.535540000000122101280.47682494.87049060.943092234.5201231.2011900.238667004.49283810000111
214575217.857903018.7672611.3606672.9046628.555256000000152106186.38443695.41070093.649735367.9868776.4580608.687480019.20495200000111
214677115.47647904.5946709.8860028.1200255.769464000000115118237.024558156.26729499.678209294.80233817.0110031.972137005.03633400000111
214778115.29991108.6745056.3542821.2634278.32287401000010396242.19719252.48296181.281111145.2537464.0304915.173891003.78539900001100
214872233.28973807.8907036.5709937.9414049.87871100000016678283.39679792.20006481.920043217.39687311.1147776.307543018.32756301001011